The additional file contains: a section where we show our fitting method performance on different synthetic data sets; a section where we show the plot of gamma and stretched exponential fit results for CG interdistance distribution of Homo sapiens; a section where we show how we calculated errors on r-squared, based on Olkin and Finnâs approximation; a final section where we collected into two tables all the informations about the analysis perfomed on the 4425 organisms. The first table contains informations about organism type and identification on NCBI website; the second contains gamma fit parameters, ratio of unkown nucleotides (%N), ratio of CG dinucleotides (%CG) and r-squared values. (PDF 1960 kb
The graph shows bulk sbGC on the y-axis plotted against corresponding cgGC on the x-axis for the cor...
Figure S1. Correspondence between Ne estimated from universally distributed genes and from the compl...
Figure S1. P value distributions calculated as part of the generation of predictive models for metab...
Background: Statistical approaches to genetic sequences have revealed helpful to gain deeper insight...
Figure S3. Population average and standard deviation of the cross-feeding factor C i as a function o...
PCoA based on Aitchison distance applied to most abundant bacterial families. To avoid taking the lo...
Goodness of fit. Probability plots confirming that overall, class I bacterial chromosome CRISPR arra...
Model fits to the observed data. Posterior mean (red), 50% (dark blue) and 95% credible (light blue)...
The location and SNP number for five susceptibility genes belonging to the network associated with l...
Additional file 1: Table S1. a. The average of the intra- specific nucleotide percentage of pairwise...
Four bacterial families comprise most the biological variation in our study. Relative biological var...
File S1, Derivations of the analytical model for the effect of ploidy and the average level of domin...
Figure S1. Histograms of Spearman correlations between normalization factors and raw counts of non-d...
An inverse relationship between biological variation and initial relative abundance. 5, 25, 50, 75, ...
Figure S2. Scatterplot of normalization factors for each pair of scaling methods. Normalization fact...
The graph shows bulk sbGC on the y-axis plotted against corresponding cgGC on the x-axis for the cor...
Figure S1. Correspondence between Ne estimated from universally distributed genes and from the compl...
Figure S1. P value distributions calculated as part of the generation of predictive models for metab...
Background: Statistical approaches to genetic sequences have revealed helpful to gain deeper insight...
Figure S3. Population average and standard deviation of the cross-feeding factor C i as a function o...
PCoA based on Aitchison distance applied to most abundant bacterial families. To avoid taking the lo...
Goodness of fit. Probability plots confirming that overall, class I bacterial chromosome CRISPR arra...
Model fits to the observed data. Posterior mean (red), 50% (dark blue) and 95% credible (light blue)...
The location and SNP number for five susceptibility genes belonging to the network associated with l...
Additional file 1: Table S1. a. The average of the intra- specific nucleotide percentage of pairwise...
Four bacterial families comprise most the biological variation in our study. Relative biological var...
File S1, Derivations of the analytical model for the effect of ploidy and the average level of domin...
Figure S1. Histograms of Spearman correlations between normalization factors and raw counts of non-d...
An inverse relationship between biological variation and initial relative abundance. 5, 25, 50, 75, ...
Figure S2. Scatterplot of normalization factors for each pair of scaling methods. Normalization fact...
The graph shows bulk sbGC on the y-axis plotted against corresponding cgGC on the x-axis for the cor...
Figure S1. Correspondence between Ne estimated from universally distributed genes and from the compl...
Figure S1. P value distributions calculated as part of the generation of predictive models for metab...